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A wider range of R programming options enables developers to use a full-featured, integrated R development environment within SPSS Statistics.

IBM SPSS Base Overview, Features and Benefits

IBM® SPSS® Statistics Base is easy to use and forms the foundation for many types of statistical analyses.

The procedures within IBM SPSS Statistics Base will enable you to get a quick look at your data, formulate hypotheses for additional testing, and then carry out a number of statistical and analytic procedures to help clarify relationships between variables, create clusters, identify trends and make predictions.

Quickly access and analyze massive datasets

Easily prepare and manage your data for analysis

Analyze data with a comprehensive range of statistical procedures

Easily build charts with sophisticated reporting capabilities

Discover new insights in your data with tables, graphs, cubes and pivoting technology

Tests to Predict Numerical Outcomes and Identify Groups:

IBM SPSS Statistics Base contains procedures for the projects you are working on now and any new ones to come. You can be confident that you'll always have the analytic tools you need to get the job done quickly and effectively.

Factor Analysis - Used to identify the underlying variables, or factors, that explain the pattern of correlations within a set of observed variables. In IBM SPSS Statistics Base, the factor analysis procedure provides a high degree of flexibility, offering:

Seven methods of factor extraction

Five methods of rotation, including direct oblimin and promax for nonorthogonal rotations

Three methods of computing factor scores. Also, scores can be saved as variables for further analysis

K-means Cluster Analysis - Used to identify relatively homogeneous groups of cases based on selected characteristics, using an algorithm that can handle large numbers of cases but which requires you to specify the number of clusters. Select one of two methods for classifying cases, either updating cluster centers iteratively or classifying only.

Hierarchical Cluster Analysis - Used to identify relatively homogeneous groups of cases (or variables) based on selected characteristics, using an algorithm that starts with each case in a separate cluster and combines clusters until only one is left. Analyze raw variables or choose from a variety of standardizing transformations. Distance or similarity measures are generated by the Proximities procedure. Statistics are displayed at each stage to help you select the best solution.

TwoStep Cluster Analysis - Group observations into clusters based on nearness criterion, with either categorical or continuous level data; specify the number of clusters or let the number be chosen automatically.

Discriminant - Offers a choice of variable selection methods, statistics at each step and in a final summary; output is displayed at each step and/or in final form.

Ordinal regression—PLUM - Choose from seven options to control the iterative algorithm used for estimation, to specify numerical tolerance for checking singularity, and to customize output; five link functions can be used to specify the model.

Nearest Neighbor analysis - Use for prediction (with a specified outcome) or for classification (with no outcome specified); specify the distance metric used to measure the similarity of cases; and control whether missing values or categorical variables are treated as valid values.

Procedures Included:

General linear models (GLM) – Provides you with more flexibility to describe the relationship between a dependent variable and a set of independent variables. The GLM gives you flexible design and contrast options to estimate means and variances and to test and predict means. You can also mix and match categorical and continuous predictors to build models. Because GLM doesn't limit you to one data type, you have options that provide you with a wealth of model-building possibilities.

The linear mixed models procedure expands the general linear models used in the GLM procedure so that you can analyze data that exhibit correlation and non-constant variability. If you work with data that display correlation and non-constant variability, such as data that represent students nested within classrooms or consumers nested within families, use the linear mixed models procedure to model means, variances and covariances in your data.

Its flexibility means you can formulate dozens of models, including split-plot design, multi-level models with fixed-effects covariance, and randomized complete blocks design. You can also select from 11 non-spatial covariance types, including first-order ante-dependence, heterogeneous, and first-order autoregressive. You'll reach more accurate predictive models because it takes the hierarchical structure of your data into account.

You can also use linear mixed models if you're working with repeated measures data, including situations in which there are different numbers of repeated measurements, different intervals for different cases, or both. Unlike standard methods, linear mixed models use all your data and give you a more accurate analysis.

Generalized linear models (GENLIN): GENLIN covers not only widely used statistical models, such as linear regression for normally distributed responses, logistic models for binary data, and loglinear model for count data, but also many useful statistical models via its very general model formulation. The independence assumption, however, prohibits generalized linear models from being applied to correlated data.

Loglinear and logit models to count data by means of a generalized linear models approach (GENLOG)

Survival analysis procedures:

Cox regression with time-dependent covariates

Kaplan-Meier

Life Tables

IBM SPSS Regression Overview, Features and Benefits

More Statistics for Data Analysis

Expand the capabilities of IBM® SPSS® Statistics Base for the data analysis stage in the analytical process. Using IBM SPSS Regression with IBM SPSS Statistics Base gives you an even wider range of statistics so you can get the most accurate response for specific data types.

IBM SPSS Regression includes:

Multinomial logistic regression (MLR): Regress a categorical dependent variable with more than two categories on a set of independent variables. This procedure helps you accurately predict group membership within key groups.
You can also use stepwise functionality, including forward entry, backward elimination, forward stepwise or backward stepwise, to find the best predictor from dozens of possible predictors. If you have a large number of predictors, Score and Wald methods can help you more quickly reach results. You can access your model fit using Akaike information criterion (AIC) and Bayesian information criterion (BIC; also called Schwarz Bayesian criterion, or SBC).

Binary logistic regression: Group people with respect to their predicted action. Use this procedure if you need to build models in which the dependent variable is dichotomous (for example, buy versus not buy, pay versus default, graduate versus not graduate). You can also use binary logistic regression to predict the probability of events such as solicitation responses or program participation.
With binary logistic regression, you can select variables using six types of stepwise methods, including forward (the procedure selects the strongest variables until there are no more significant predictors in the dataset) and backward (at each step, the procedure removes the least significant predictor in the dataset) methods. You can also set inclusion or exclusion criteria. The procedure produces a report telling you the action it took at each step to determine your variables.

Nonlinear regression (NLR) and constrained nonlinear regression (CNLR): Estimate nonlinear equations. If you are you working with models that have nonlinear relationships, for example, if you are predicting coupon redemption as a function of time and number of coupons distributed, estimate nonlinear equations using one of two IBM SPSS Statistics procedures: nonlinear regression (NLR) for unconstrained problems and constrained nonlinear regression (CNLR) for both constrained and unconstrained problems.
NLR enables you to estimate models with arbitrary relationships between independent and dependent variables using iterative estimation algorithms, while CNLR enables you to:

Weighted least squares (WLS): If the spread of residuals is not constant, the estimated standard errors will not be valid. Use Weighted Least Square to estimate the model instead (for example, when predicting stock values, stocks with higher shares values fluctuate more than low value shares.)

Two-stage least squares (2LS): Use this technique to estimate your dependent variable when the independent variables are correlated with the regression error terms.
For example, a book club may want to model the amount they cross-sell to members using the amount that members spend on books as a predictor. However, money spent on other items is money not spent on books, so an increase in cross-sales corresponds to a decrease in book sales. Two-Stage Least-Squares Regression corrects for this error.

Probit analysis: Probit analysis is most appropriate when you want to estimate the effects of one or more independent variables on a categorical dependent variable.
For example, you would use probit analysis to establish the relationship between the percentage taken off a product, and whether a customer will buy as the prices decreases. Then, for every percent taken off the price you can work out the probability that a consumer will buy the product.

IBM SPSS Regression includes additional diagnostics for use when developing a classification table

More than a simple reporting tool, IBM SPSS Custom Tables combines comprehensive analytical capabilities with interactive table-building features to help you learn from your data and communicate the results of your analyses as professional-looking tables that are easy to read and interpret.

Compare means or proportions for demographic groups, customer segments, time periods or other categorical variables when you include inferential statistics

Select summary statistics - from simple counts for categorical variables to measures of dispersion - and sort categories by any summary statistic used

Export tables to Microsoft® Word, Excel®, PowerPoint® or HTML for use in reports

IBM SPSS Custom Tables is an analytical tool that helps you augment your reports with information your readers need to make more informed decisions.

Use inferential statistics—also known as significance testing—in your tables to perform common analyses: Compare means or proportions for demographic groups, customer segments, time periods, or other categorical variables; and identify trends, changes, or major differences in your data. IBM SPSS Custom Tables includes the following significance tests:

Chi-square test of independence

Comparison of column means (t test)

Comparison of column proportions (z test)

You can also choose from a variety of summary statistics, which include everything from simple counts for categorical variables to measures of dispersion. Summary statistics are included for:

Categorical variables

Multiple response sets

Scale variables

Custom total summaries for categorical variables

When your analysis is complete, you can use IBM SPSS Custom Tables to create customized tabular reports suitable for a variety of audiences—including those without a technical background.

IBM SPSS Data Preparation Overview, Features, and Benefits

IBM® SPSS® Data Preparation gives analysts advanced techniques to streamline the data preparation stage of the analytical process. All researchers have to prepare their data before analysis. While basic data preparation tools are included in IBM SPSS Statistics Base, IBM SPSS Data Preparation provides specialized techniques to prepare your data for more accurate analyses and results.

Use the specialized data preparation techniques in IBM SPSS Data Preparation to facilitate data preparation in the analytical process. IBM SPSS Data Preparation easily plugs into IBM SPSS Statistics Base so you can seamlessly work in the IBM SPSS environment.

Perform Data Checks

Data validation has typically been a manual process. You might run a frequency on your data, print the frequencies, circle what needs to be fixed and check for case IDs. This approach is time consuming and prone to errors. And since every analyst in your organization could use a slightly different method, maintaining consistency from project to project may be a challenge.

To eliminate manual checks, use the IBM SPSS Data Preparation Validate Data procedure. This enables you to apply rules to perform data checks based on each variable's measure level (whether categorical or continuous).

For example, if you're analyzing data that has variables on a five-point Likert scale, use the Validate Data procedure to apply a rule for five-point scales and flag all cases that have values outside of the 1-5 range. You can receive reports of invalid cases as well as summaries of rule violations and the number of cases affected. You can specify validation rules for individual variables (such as range checks) and cross-variable checks (for example, "retired 30 year-olds").

With this knowledge you can determine data validity and remove or correct suspicious cases at your discretion before analysis.

Quickly Find Multivariate Outliers

Prevent outliers from skewing analyses when you use the IBM SPSS Data Preparation Anomaly Detection procedure. This searches for unusual cases based upon deviations from similar cases, and gives reasons for such deviations. You can flag outliers by creating a new variable. Once you have identified unusual cases, you can further examine them and determine if they should be included in your analyses.

Pre-process Data before Model Building

In order to use algorithms that are designed for nominal attributes (such as Naïve Bayes and logit models), you must bin your scale variables before model building. If scale variables aren't binned, algorithms such as multinomial logistic regression will take an extremely long time to process or they might not converge. This is especially true if you have a large dataset. In addition, the results you receive may be difficult to read or interpret.

IBM SPSS Data Preparation Optimal Binning, however, enables you to determine cutpoints to help you reach the best possible outcome for algorithms designed for nominal attributes.

With this procedure, you can select from three types of binning for pre processing data:

Unsupervised -- create bins with equal counts

Supervised -- take the target variable into account to determine cutpoints. This method is more accurate than unsupervised; however, it is also more computationally intensive.

Hybrid approach -- combines the unsupervised and supervised approaches. This method is particularly useful if you have a large number of distinct values.

IBM SPSS Missing Values

IBM® SPSS® Missing Values is used by survey researchers, social scientists, data miners, market researchers and others to validate data.

Missing data can seriously affect your models – and your results. Ignoring missing data, or assuming that excluding missing data is sufficient, risks reaching invalid and insignificant results. To ensure that you take missing values into account, make IBM SPSS Missing Values part of your data management and preparation.

Uncover Missing Data Patterns

Easily examine data from several different angles using one of six diagnostic reports, then estimate summary statistics and impute missing values

Quickly diagnose serious missing data imputation problems

Replace missing values with estimates

Display a snapshot of each type of missing value and any extreme values for each case

Remove hidden bias by replacing missing values with estimates to include all groups ¬– even those with poor responsiveness

Uncover Missing Data Patterns

With IBM SPSS Missing Values, you can easily examine data from several different angles using one of six diagnostic reports to uncover missing data patterns. You can then estimate summary statistics and impute missing values through regression or expectation maximization algorithms (EM algorithms).

Quickly and Easily Diagnose Your Missing Data

Quickly diagnose a serious missing data problem using the data patterns report, which provides a case-by-case overview of your data. This report helps you determine the extent of missing data; it displays a snapshot of each type of missing value and any extreme values for each case.

Reach More Valid Conclusions

Replace missing values with estimates and increase the chance of receiving statistically significant results. Remove hidden bias from your data by replacing missing values with estimates to include all groups in your analysis – even those with poor responsiveness.

Use Multiple Imputation to Replace Missing Data Values

IBM SPSS Missing Values' multiple imputation procedure will help you understand patterns of “missingness” in your dataset and enable you to replace missing values with plausible estimates. It offers a fully automatic imputation mode that chooses the most suitable imputation method based on characteristics of your data, while also allowing you to customize your imputation model.

Several complete datasets are generated (typically, three to five), each with a different set of replacement values. Next, you can model the individual datasets, using techniques such as linear regression, to produce parameter estimates for each dataset. Then you can obtain final parameter estimates. This involves pooling the individual sets of parameter estimates obtained in step two and computing inferential statistics that take into account variation within and between imputations.

Analysis of the individual datasets and pooling of the results are supported via existing IBM SPSS Statistics procedures such as REGRESSION. When operating on datasets with imputed values, existing procedures will automatically produce pooled parameter estimates.

Fill in the Blanks for Improved Data Management

IBM SPSS Missing Values has the statistics you need to fill in missing data:

Univariate: compute count, mean, standard deviation, and standard error of mean for all cases excluding those containing missing values, count and percent of missing values, and extreme values for all variables

Estimate the means, covariance matrix, and correlation matrix of quantitative variables with missing values, assuming normal distribution, t distribution with degrees of freedom, or a mixed-normal distribution with any mixture proportion and any standard deviation ratio

Impute missing data and save the completed data as a file

Regression algorithm

Estimate the means, covariance matrix, and correlation matrix of variables set as dependent; set number of predictor variables; set random elements as normal, t, residuals, or none

IBM SPSS Missing Values also has features that enable you to analyze patterns and manage data, including the ability to:

Display missing data and extreme cases for all cases and all variables using the data patterns table

Determine differences between missing and non-missing groups for a related variable with the separate t test table

Assess how much missing data for one variable relates to the missing data of another variable using the percent mismatch of patterns table

IBM SPSS Forecasting

IBM® SPSS® Forecasting enables analysts to predict trends and develop forecasts quickly and easily -- without being an expert statistician.

Reliable forecasts can have a major impact on your organization’s ability to develop and implement successful strategies. Unlike spreadsheet programs, IBM SPSS Forecasting has the advanced statistical techniques needed to work with time-series data regardless of your level of expertise.

Analyze historical data and predict trends faster, and deliver information in ways that your organization’s decision makers can understand and use

Model hundreds of different time series at once, rather than having to run the procedure for one variable at a time

Save models to a central file so that forecasts can be updated when data changes, without having to re-set parameters or re-estimate models

Write scripts so that models can be updated with new data automatically

IBM SPSS Decision Trees

IBM SPSS Forecasting offers a number of capabilities that enable both novice and experienced users to quickly develop reliable forecasts using time-series data. It is a fully integrated module of IBM SPSS Statistics, giving you all of IBM SPSS Statistics’ capabilities plus features specifically designed to support forecasting.

New to Building Models from Time-series Data?

IBM SPSS Forecasting helps you by:

Generating reliable models, even if you’re not sure how to choose exponential smoothing parameters or ARIMA orders, or how to achieve stationarity

Procedures and Statistics for Analyzing Time-series Data

Techniques Tailored to Time-series Analysis

IBM SPSS Statistics has the procedures you need to realize the most benefit from your time-series analysis. It generates statistics and normal probability plots so that you can easily judge model fit. You can even limit output to see only the worst-fitting models -- those that require further examination. Automatically generated high-resolution charts enhance your output.

Procedures available in IBM SPSS Forecasting include:

TSMODEL - Use the Expert Modeler to model a set of time-series variables, using either ARIMA or exponential smoothing techniques

TSAPPLY - Apply saved models to new or updated data

SEASON - Estimate multiplicative or additive seasonal factors for periodic time series

SPECTRA - Decompose a time series into its harmonic components, which are sets of regular periodic functions at different wavelengths or periods

This module features highly visual classification and decision trees. These trees enable you to present categorical results in an intuitive manner, so you can more clearly explain categorical analysis to non-technical audiences.

IBM SPSS Decision Trees enables you to explore results and visually determine how your model flows. This helps you find specific subgroups and relationships that you might not uncover using more traditional statistics. The module includes four established tree-growing algorithms.

Use IBM SPSS Decision Trees if you need to identify groups and sub-groups. Applications include:

Although IBM SPSS Direct Marketing relies on powerful analytics, you don't need to be a statistician or programmer to use it. The intuitive interface guides you every step of the way, and the new Scoring Wizard makes it easy to build models to score your data. After you run an analysis, the significance of the output is clearly explained.

IBM SPSS Direct Marketing includes a combination of specifically chosen procedures that enable database and direct marketers to conduct data preparation and analysis activities. You can do this using only IBM SPSS Direct Marketing, or you can use it in conjunction with other applications in the IBM SPSS Statistics product family.

RFM Analysis: Score customers according to the recency, frequency and monetary value of their purchases.

Segment customers or contacts: Create "clusters" of those who are like each other, and distinctly different from others.

Profile customers or contacts: Identify shared characteristics, to improve the targeting of marketing offers and campaigns.

Identify those who are likely to purchase: Develop propensity scores and improve the focus and timing of your campaigns.

IBM SPSS Complex Samples provides the specialized planning tools and statistics you need when working with complex sample designs, such as stratified, clustered or multistage sampling.

This module of IBM SPSS Statistics is an indispensable for survey and market researchers, public opinion researchers or social scientists seeking to reach more accurate conclusions when working with sample survey methodology. You can more accurately work with numerical and categorical outcomes in complex sample designs using two algorithms for analysis and prediction. In addition, you can use this module’s techniques to predict time to an event

Only IBM® SPSS® Complex Samples makes understanding and working with your complex sample survey results easy. Through the intuitive interface, you can analyze data and interpret results. Choose from one of several wizards to make it easier to create plans, analyze data and interpret results.

When you're finished, you can publish public-use datasets and include your sampling and analysis plans. These plans act as a template and allow you to save all the decisions made when creating the plan – define it once and you're done. This saves time and improves accuracy for yourself and others who may want to plug your plans into the data to replicate results or pick up where you left off.

Use the following types of sample design information with IBM SPSS Complex Samples:

Stratified sampling – Increase the precision of your sample or ensure a representative sample from key groups by choosing to sample within subgroups of the survey population.

Multistage sampling – Select an initial or first-stage sample based on groups of elements in your population; then create a second-stage sample by drawing a sub-sample from each selected unit in the first-stage sample. By repeating this option, you can select a higher-stage sample.

Everything You Need for Planning

To help you through the planning stage in the analytical process, IBM SPSS Complex Samples provides you with specialized tools and procedures for working with sample survey data:

IBM SPSS Complex Samples Plan (CSPLAN) – Use this procedure to specify the sampling frame to create a complex sample design or analysis specification used by companion procedures in IBM SPSS Complex Samples.

Sampling Plan Wizard – If you are creating your own samples, use the Sampling Plan Wizard to define the scheme and draw the sample.

Analysis Preparation Wizard – If you're using public-use datasets that already have samples, use the Analysis Plan Wizard to specify how the samples were defined and how standard errors should be estimated.